From the VisDB predictions, we can obtain the 3D coordinates and visibility of human joints and vertices.
While the partial-body outputs are faithful to the input image from the front view, they sometimes look abnormal from a side view or contain rough surfaces. To regularize the body shape and complete the truncated parts, we perform model fitting on the visible dense body predictions.
Given the coordinates and visibility of joints and vertices, we train a regression network to estimate SMPL pose \(\theta \in \mathbb {R}^{72}\)  and shape \(\beta \in \mathbb {R}^{10}\)  parameters.
The regressed parameters are then forwarded to the SMPL model to generate the mesh coordinates denoted as \(\text{SMPL}(\theta ,\beta ) \in \mathbb {R}^{N_V \times 3}\) .
Unlike prior art [1]} which regresses a SMPL model from all the joints regardless of their visibility, our VisDB representation allows us to fit the visible partial body only.
The training objectives of the SMPL regressor include SMPL parameter error, vertex error, joint error, and the negative log-likelihood of a pose prior distribution.
The SMPL parameter loss \(\mathcal {L}_{smpl}\)  is defined as:
\(\mathcal {L}_{smpl} = \Vert \theta - \theta ^*\Vert _1 + \Vert \beta - \beta ^*\Vert _1 ,\) 
